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Python으로 배우는 확률 기초

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업데이트됨 2024. 8.

확률 변수, 평균과 분산, 확률 분포, 조건부 확률 등 확률의 기본 개념을 배웁니다.

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확률은 무작위 실험의 결과에서 나타나는 규칙성을 연구하는 학문이에요. 이 강의에서는 동전 던지기 같은 고전적 예시로 시작해, 확률변수와 평균·분산 계산, 확률분포, 조건부확률 등 핵심 개념을 배웁니다. 또한 확률에서 매우 중요한 두 가지 결과인 대수의 법칙과 중심극한정리를 다룹니다. 확률은 데이터 사이언스와 Machine Learning의 핵심에 있으므로, 이 개념들은 모델을 더 탄탄하게 이해하고 적용하는 데 도움이 될 거예요. 세상은 온통 확률로 가득해요. 확률을 공부하면 세상을 보는 눈이 달라집니다. 이제 무작위의 세계로 떠나 볼까요?

선수 조건

Introduction to Statistics in Python

1

Let's start flipping coins

A coin flip is the classic example of a random experiment. The possible outcomes are heads or tails. This type of experiment, known as a Bernoulli or binomial trial, allows us to study problems with two possible outcomes, like “yes” or “no” and “vote” or “no vote.” This chapter introduces Bernoulli experiments, binomial distributions to model multiple Bernoulli trials, and probability simulations with the scipy library.

Let’s flip a coin in Python

Flipping coins

Using binom to flip even more coins

Probability mass and distribution functions

Predicting the probability of defects

Predicting employment status

Predicting burglary conviction rate

Expected value, mean, and variance

Calculating the expected value and variance

Calculating the sample mean

Checking the result

Calculating the mean and variance of a sample

2

Calculate some probabilities

In this chapter you'll learn to calculate various kinds of probabilities, such as the probability of the intersection of two events and the sum of probabilities of two events, and to simulate those situations. You'll also learn about conditional probability and how to apply Bayes' rule.

Calculating probabilities of two events

Any overlap?

Measuring a sample

Joint probabilities

Deck of cards

Conditional probabilities

Delayed flights

Contingency table

Total probability law

Formula 1 engines

Bayes' rule

Conditioning

Factories and parts

Swine flu blood test

3

Important probability distributions

Until now we've been working with binomial distributions, but there are many probability distributions a random variable can take. In this chapter we'll introduce three more that are related to the binomial distribution: the normal, Poisson, and geometric distributions.

Normal distributions

Range of values

Plotting normal distributions

Within three standard deviations

Normal probabilities

Restaurant spending example

Smartphone battery example

Adults' heights example

Poisson distributions

ATM example

Highway accidents example

Generating and plotting Poisson distributions

Geometric distributions

Catching salmon example

Free throws example

Generating and plotting geometric distributions

4

Probability meets statistics

No that you know how to calculate probabilities and important properties of probability distributions, we'll introduce two important results: the law of large numbers and the central limit theorem. This will expand your understanding on how the sample mean converges to the population mean as more data is available and how the sum of random variables behaves under certain conditions.We will also explore connections between linear and logistic regressions as applications of probability and statistics in data science.

From sample mean to population mean

Generating a sample

Calculating the sample mean

Plotting the sample mean

Adding random variables

Sample means

Sample means follow a normal distribution

Adding dice rolls

Linear regression

Fitting a model

Predicting test scores

Studying residuals

Logistic regression

Fitting a logistic model

Predicting if students will pass

Passing two tests

Wrapping up

Python으로 배우는 확률 기초

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